Mineral analysis systems, such as the Qemscan and MLA available from FEI Company, Hillsboro, Oreg., have been used for many years to analyze mineral samples. To determine the type and relative quantity of minerals present in a mine, a sample in the form of small granules, is fixed in epoxy in a mold and the mold is placed in a vacuum chamber. An electron beam is directed toward a sample and, in a process called “energy dispersive x-ray spectroscopy” or “EDS,” the energies of x-rays coming from the sample in response to the electron beam are measured and plotted in a histogram to form a spectrum. The measured spectrum can be compared to the known spectra of various elements to determine which elements and minerals are present.
Mineral analysis systems, such as the QEMSCAN® (Quantitative Evaluation of Minerals by Scanning electron microscopy) and MLA (Mineral Liberation Analyzer) from FEI Company, the assignee of the present invention, have been used for many years to determine minerals present in mines in order to determine the presence of valuable minerals. Such systems direct an electron beam toward the sample and measure the energy of x-rays coming from the material in response to the electron beam. One such process is called “energy dispersive x-ray analysis” or “EDS,” which can be used for elemental analysis or chemical characterization of a sample. Backscattered electron (BSE) detectors are also used for mineral analysis in conjunction with electron beam columns. The intensity of the BSE signal is a function of the average atomic number of the material under the electron beam, and this relationship can be used to develop a useful mineral identification method.
EDS systems rely on the emission of X-rays from a sample to perform elemental analysis. Each element has a unique atomic structure, which allows x-rays that are characteristic of an element's atomic structure to be uniquely identified from one another. To stimulate the emission of x-rays from a sample, a beam of charged particles is focused onto the sample, which causes electrons from inner shells to be ejected. Electrons from outer shells seek to fill this electron void, and the difference in energy between the higher energy shell and the lower energy shell is released as an x-ray, which can be detected by an EDS detector.
QEMSCAN® comprises a SEM, multiple EDS detectors, and software for controlling automated data acquisition. This technology identifies and quantifies elements within an acquired spectrum and then matches this data against a list of mineral definitions with fixed elemental ranges. The size of the ranges depends directly on the number of x-rays in the spectrum and cannot be applied to higher quality spectra without creating a new mineral definition. Thus, it is not possible to define a universal database for an arbitrary number of X-ray counts. Furthermore, the match is not given as a probability value, it is given as either true or false, and it picks the first match it finds even if a better match might be present elsewhere in the mineral database.
MLA technology also combines a SEM, multiple EDS detectors, and automated quantitative mineralogy software. MLA computes a probability match between a measured mineral spectrum and a reference mineral spectrum. This method works reasonably, but the numerical value obtained tends to be dominated by the size of the largest peak in the x-ray spectrum.
The acquisition time of a suitable BSE signal is typically on the order of microseconds per pixel. However, EDS systems are usually slower and have a longer acquisition time, typically on the order of several seconds per pixel to uniquely discriminate the spectrum from all other mineral spectra. As a result, the time required to collect an x-ray spectrum to uniquely identify a mineral reduces the number of pixels that can be measured substantially. EDS systems are also typically insensitive to light atoms. Because of the advantages of both EDS detectors and BSE detectors, it is sometimes useful to use both BSE and x-ray spectra to accurately identify minerals, which requires more time and becomes a difficult problem to solve with a commercially viable approach.
A mineral classification system must be capable of comparing each unknown measured spectrum to a library of known mineral spectrums, and then making a selection based on which known mineral is most similar to the measured spectrum. Typically, to find the most similar spectrum requires the use of a metric that represents the degree of similarity between the measured data and the known material.
Currently, there are various ways to compare two spectrums directly, either by calculating a distance metric or a similarity metric. An example of a method of comparison used in the prior art is to take the sum of the differences between the two spectrums as a distance. The Mineral Liberation Analyzer manufactured by FEI Company, Inc., the assignee of the present invention, uses a chi-squared statistical test to compare the value at each energy channel of the measured spectrum to the value at the corresponding channel of the known mineral spectrum. These prior art approaches are based around comparing the spectrums on a channel by channel basis. The problem of using a comparison on a channel by channel basis is that there is no guarantee that all required peaks in the mineral spectrum are present in the measured spectrum. It is possible that a measured spectrum appears to be similar to a mineral yet it is missing an element that is required by the definition of that mineral, or has an additional element not found in that definition of a mineral.
In the XBSE_STD measurement mode of the MLA, each data point is compared against a mineral list. If the data point is not similar to any mineral, then a new mineral entry is created and a high quality EDS spectrum is immediately measured from the sample. However, there are several significant limitations of this approach. First, the user is presented with hundreds of unknown data points and there is no way to distinguish which ones occur most frequently and which ones are outliers. Second, the analysis cannot be performed offline as it requires access to the SEM to collect the high quality data during measurement. Finally, only the raw data is presented to the user and there is no analytical tool to give elemental composition. Thus, there is a need for an improved mineral identification method.